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      "nbconvert_exporter": "python",
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      "version": "3.6.5"
    },
    "colab": {
      "name": "Neural_Style_Transfer.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": []
    },
    "accelerator": "GPU"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kaj3YpiT6lqj",
        "colab_type": "text"
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      "source": [
        "# Neural Style Transfer\n",
        "\n",
        "If you use social sharing apps or happen to be an amateur photographer, you are familiar with filters. Filters can alter the color styles of photos to make the background sharper or people's faces whiter. However, a filter generally can only change one aspect of a photo. To create the ideal photo, you often need to try many different filter combinations. This process is as complex as tuning the hyper-parameters of a model.\n",
        "\n",
        "In this section, we will discuss how we can use convolution neural networks\n",
        "(CNNs) to automatically apply the style of one image to another image, an\n",
        "operation known as style transfer :cite:`Gatys.Ecker.Bethge.2016`. Here, we need two input images, one content image and one style image. We use a neural network to alter the content image so that its style mirrors that of the style image. In :numref:`fig_style_transfer`, the content image is a landscape photo the author took in Mount Rainier National Part near Seattle. The style image is an oil painting of oak trees in autumn. The output composite image retains the overall shapes of the objects in the content image, but applies the oil painting brushwork of the style image and makes the overall color more vivid.\n",
        "\n",
        "![Content and style input images and composite image produced by style transfer. ](../img/style-transfer.svg)\n",
        "\n",
        ":label:`fig_style_transfer`\n",
        "\n",
        "\n",
        "## Technique\n",
        "\n",
        "The CNN-based style transfer model is shown in :numref:`fig_style_transfer_model`.\n",
        "First, we initialize the composite image. For example, we can initialize it as the content image. This composite image is the only variable that needs to be updated in the style transfer process, i.e. the model parameter to be updated in style transfer. Then, we select a pre-trained CNN to extract image features. These model parameters do not need to be updated during training. The deep CNN uses multiple neural layers that successively extract image features. We can select the output of certain layers to use as content features or style features. If we use the structure in :numref:`fig_style_transfer_model`, the pretrained neural network contains three convolutional layers. The second layer outputs the image content features, while the outputs of the first and third layers are used as style features. Next, we use forward propagation (in the direction of the solid lines) to compute the style transfer loss function and backward propagation (in the direction of the dotted lines) to update the model parameter, constantly updating the composite image. The loss functions used in style transfer generally have three parts: 1. Content loss is used to make the composite image approximate the content image as regards content features. 2. Style loss is used to make the composite image approximate the style image in terms of style features. 3. Total variation loss helps reduce the noise in the composite image. Finally, after we finish training the model, we output the style transfer model parameters to obtain the final composite image.\n",
        "\n",
        "![CNN-based style transfer process. Solid lines show the direction of forward propagation and dotted lines show backward propagation. ](../img/neural-style.svg)\n",
        "\n",
        ":label:`fig_style_transfer_model`\n",
        "\n",
        "\n",
        "\n",
        "Next, we will perform an experiment to help us better understand the technical details of style transfer.\n",
        "\n",
        "## Read the Content and Style Images\n",
        "\n",
        "First, we would import the required dependencies and read the content and style images. By printing out the image coordinate axes, we can see that they have different dimensions."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XfcEW5z56lqm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import sys\n",
        "sys.path.insert(0, '..')\n",
        "\n",
        "import numpy as np\n",
        "\n",
        "import d2l\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torchvision.transforms as transforms\n",
        "import torchvision.models as models\n",
        "import torch.optim as optim\n",
        "\n",
        "from PIL import Image\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "%matplotlib inline"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "attributes": {
          "classes": [],
          "id": "",
          "n": "1"
        },
        "scrolled": true,
        "id": "9Z4vV1Ka6lq1",
        "colab_type": "code",
        "outputId": "39fe86f1-52e4-4fba-bfea-0839c571b648",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        }
      },
      "source": [
        "d2l.set_figsize((3.5, 2.5))\n",
        "content_img = Image.open('../img/rainier.jpg')\n",
        "d2l.plt.imshow(np.asarray(content_img));"
      ],
      "execution_count": 208,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "attributes": {
          "classes": [],
          "id": "",
          "n": "2"
        },
        "id": "_0_4r4iK6lrF",
        "colab_type": "code",
        "outputId": "5d023a03-3785-4cf5-a0f0-c7401d5d29d9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 249
        }
      },
      "source": [
        "style_img = Image.open('../img/autumn_oak.jpg')\n",
        "d2l.plt.imshow(np.asarray(style_img));"
      ],
      "execution_count": 209,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 252x180 with 1 Axes>"
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      "cell_type": "markdown",
      "metadata": {
        "id": "I_81mNpu6lrU",
        "colab_type": "text"
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      "source": [
        "## Preprocessing and Postprocessing\n",
        "\n",
        "Below, we define the functions for image preprocessing and postprocessing. The `preprocess` function normalizes each of the three RGB channels of the input images and transforms the results to a format that can be input to the CNN. The `postprocess` function restores the pixel values in the output image to their original values before normalization. Because the image printing function requires that each pixel has a floating point value from 0 to 1, we use the `clamp` function to replace values smaller than 0 or greater than 1 with 0 or 1, respectively."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "attributes": {
          "classes": [],
          "id": "",
          "n": "3"
        },
        "id": "9DSWgmWM6lrX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "device = d2l.try_gpu()\n",
        "\n",
        "rgb_mean = torch.tensor([0.485, 0.456, 0.406]).view(-1, 1, 1)\n",
        "rgb_std = torch.tensor([0.229, 0.224, 0.225]).view(-1, 1, 1)\n",
        "\n",
        "def preprocess(img, image_shape):\n",
        "    img = transforms.Resize(image_shape)(img)\n",
        "    img = transforms.ToTensor()(img)\n",
        "    img = (img - rgb_mean) / rgb_std\n",
        "    return img.unsqueeze(0)\n",
        "\n",
        "def postprocess(img):\n",
        "    img = img.cpu()\n",
        "    img = (img * rgb_std + rgb_mean).clamp(0, 1)\n",
        "    img = transforms.ToPILImage()(img.squeeze(0))\n",
        "    return img"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p4SPO-rp6lrf",
        "colab_type": "text"
      },
      "source": [
        "## Extract Features\n",
        "\n",
        "We use the VGG-19 model pre-trained on the ImageNet data set to extract image features[1]."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "attributes": {
          "classes": [],
          "id": "",
          "n": "4"
        },
        "id": "qi7PquEq6lri",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "pretrained_net = models.vgg19(pretrained=True).features.to(device).eval()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hLXulwIY6lrq",
        "colab_type": "text"
      },
      "source": [
        "To extract image content and style features, we can select the outputs of certain layers in the VGG network. In general, the closer an output is to the input layer, the easier it is to extract image detail information. The farther away an output is, the easier it is to extract global information. To prevent the composite image from retaining too many details from the content image, we select a VGG network layer near the output layer to output the image content features. This layer is called the content layer. We also select the outputs of different layers from the VGG network for matching local and global styles. These are called the style layers. As we mentioned in :numref:`chapter_vgg`, VGG networks have five convolutional blocks. In this experiment, we select the last convolutional layer of the fourth convolutional block as the content layer and the first layer of each block as style layers. We can obtain the indexes for these layers by printing the `pretrained_net` instance."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "attributes": {
          "classes": [],
          "id": "",
          "n": "5"
        },
        "id": "VQo4fz0b6lrs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "style_layers, content_layers = [0, 5, 10, 19, 28], [25]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SzX0lnS66lr1",
        "colab_type": "text"
      },
      "source": [
        "During feature extraction, we only need to use all the VGG layers from the input layer to the content or style layer nearest the output layer. Below, we build a new network, `net`, which only retains the layers in the VGG network we need to use. We then use `net` to extract features."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "attributes": {
          "classes": [],
          "id": "",
          "n": "6"
        },
        "id": "loGrHS156lr3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "net = nn.Sequential()\n",
        "for i in range(max(content_layers + style_layers) + 1):\n",
        "    net.add_module(str(i), list(pretrained_net.children())[i])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bCm29Foi6lr8",
        "colab_type": "text"
      },
      "source": [
        "Given input `X`, if we simply call the forward computation `net(X)`, we can only obtain the output of the last layer. Because we also need the outputs of the intermediate layers, we need to perform layer-by-layer computation and retain the content and style layer outputs."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "attributes": {
          "classes": [],
          "id": "",
          "n": "7"
        },
        "id": "k38_e52z6lsD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def extract_features(X, content_layers, style_layers):\n",
        "    contents = []\n",
        "    styles = []\n",
        "    for i in range(len(net)):\n",
        "        X = net[i](X)\n",
        "        if i in style_layers:\n",
        "            styles.append(X)\n",
        "        if i in content_layers:\n",
        "            contents.append(X)\n",
        "    return contents, styles"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r5xu2d4z6ltN",
        "colab_type": "text"
      },
      "source": [
        "Next, we define two functions: The `get_contents` function obtains the content features extracted from the content image, while the `get_styles` function obtains the style features extracted from the style image. Because we do not need to change the parameters of the pre-trained VGG model during training, we can extract the content features from the content image and style features from the style image before the start of training. As the composite image is the model parameter that must be updated during style transfer, we can only call the `extract_features` function during training to extract the content and style features of the composite image."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "attributes": {
          "classes": [],
          "id": "",
          "n": "8"
        },
        "id": "PumUWnzc6ltP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def get_contents(image_shape, device):\n",
        "    content_X = preprocess(content_img, image_shape).to(device)\n",
        "    contents_Y, _ = extract_features(content_X, content_layers, style_layers)\n",
        "    return content_X, contents_Y\n",
        "\n",
        "def get_styles(image_shape, device):\n",
        "    style_X = preprocess(style_img, image_shape).to(device)\n",
        "    _, styles_Y = extract_features(style_X, content_layers, style_layers)\n",
        "    return style_X, styles_Y"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6zyxJkFL6ltZ",
        "colab_type": "text"
      },
      "source": [
        "## Define the Loss Function\n",
        "\n",
        "Next, we will look at the loss function used for style transfer. The loss function includes the content loss, style loss, and total variation loss.\n",
        "\n",
        "### Content Loss\n",
        "\n",
        "Similar to the loss function used in linear regression, content loss uses a square error function to measure the difference in content features between the composite image and content image. The two inputs of the square error function are both content layer outputs obtained from the `extract_features` function."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "attributes": {
          "classes": [],
          "id": "",
          "n": "9"
        },
        "id": "EUpeG2mw6ltb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def content_loss(Y_hat, Y):\n",
        "    return torch.mean(torch.pow((Y_hat - Y), 2))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2JYVVxZg6lth",
        "colab_type": "text"
      },
      "source": [
        "### Style Loss\n",
        "\n",
        "Style loss, similar to content loss, uses a square error function to measure the difference in style between the composite image and style image. To express the styles output by the style layers, we first use the `extract_features` function to compute the style layer output. Assuming that the output has 1 example, $c$ channels, and a height and width of $h$ and $w$, we can transform the output into the matrix $\\boldsymbol{X}$, which has $c$ rows and $h \\cdot w$ columns. You can think of matrix $\\boldsymbol{X}$ as the combination of the $c$ vectors $\\boldsymbol{x}_1, \\ldots, \\boldsymbol{x}_c$, which have a length of $hw$. Here, the vector $\\boldsymbol{x}_i$ represents the style feature of channel $i$. In the Gram matrix of these vectors $\\boldsymbol{X}\\boldsymbol{X}^\\top \\in \\mathbb{R}^{c \\times c}$, element $x_{ij}$ in row $i$ column $j$ is the inner product of vectors $\\boldsymbol{x}_i$ and $\\boldsymbol{x}_j$. It represents the correlation of the style features of channels $i$ and $j$. We use this type of Gram matrix to represent the style output by the style layers. You must note that, when the $h \\cdot w$ value is large, this often leads to large values in the Gram matrix. In addition, the height and width of the Gram matrix are both the number of channels $c$. To ensure that the style loss is not affected by the size of these values, we define the `gram` function below to divide the Gram matrix by the number of its elements, i.e. $c \\cdot h \\cdot w$."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "attributes": {
          "classes": [],
          "id": "",
          "n": "10"
        },
        "id": "Lyo2GUE-6ltk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def gram(X):\n",
        "    num_channels, n = X.shape[1], X.nelement() // X.shape[1]\n",
        "    X = X.reshape((num_channels, n))\n",
        "    return torch.matmul(X, X.t()) / (num_channels * n)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4vSKJ8tV6ltq",
        "colab_type": "text"
      },
      "source": [
        "Naturally, the two Gram matrix inputs of the square error function for style loss are taken from the composite image and style image style layer outputs. Here, we assume that the Gram matrix of the style image, `gram_Y`, has been computed in advance."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "attributes": {
          "classes": [],
          "id": "",
          "n": "11"
        },
        "id": "Pc0X7tUf6ltt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def style_loss(Y_hat, gram_Y):\n",
        "    return torch.mean(torch.pow((gram(Y_hat) - gram_Y), 2))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6Xmm1KFk6lt6",
        "colab_type": "text"
      },
      "source": [
        "### Total Variance Loss\n",
        "\n",
        "Sometimes, the composite images we learn have a lot of high-frequency noise, particularly bright or dark pixels. One common noise reduction method is total variation denoising. We assume that $x_{i,j}$ represents the pixel value at the coordinate $(i,j)$, so the total variance loss is:\n",
        "\n",
        "$$\\sum_{i,j} \\left|x_{i,j} - x_{i+1,j}\\right| + \\left|x_{i,j} - x_{i,j+1}\\right|$$\n",
        "\n",
        "We try to make the values of neighboring pixels as similar as possible."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "attributes": {
          "classes": [],
          "id": "",
          "n": "12"
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        "id": "HKKmMGjk6lt8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def tv_loss(Y_hat):\n",
        "    return 0.5 * (torch.mean(torch.abs(Y_hat[:, :, 1:, :] - Y_hat[:, :, :-1, :])) +\n",
        "                  torch.mean(torch.abs(Y_hat[:, :, :, 1:] - Y_hat[:, :, :, :-1])))"
      ],
      "execution_count": 0,
      "outputs": []
    },
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      "cell_type": "markdown",
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        "colab_type": "text"
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      "source": [
        "### Loss Function\n",
        "\n",
        "The loss function for style transfer is the weighted sum of the content loss, style loss, and total variance loss. By adjusting these weight hyper-parameters, we can balance the retained content, transferred style, and noise reduction in the composite image according to their relative importance."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "attributes": {
          "classes": [],
          "id": "",
          "n": "13"
        },
        "id": "B54gvc-o6luB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "content_weight, style_weight, tv_weight = 1, 1e3, 10\n",
        "\n",
        "def compute_loss(X, contents_Y_hat, styles_Y_hat, contents_Y, styles_Y_gram):\n",
        "    # Calculate the content, style, and total variance losses respectively\n",
        "    contents_l = np.array([content_loss(Y_hat, Y) * content_weight for Y_hat, Y in zip(contents_Y_hat, contents_Y)])\n",
        "    styles_l = np.array([style_loss(Y_hat, Y) * style_weight for Y_hat, Y in zip(styles_Y_hat, styles_Y_gram)])\n",
        "    tv_l = tv_loss(X) * tv_weight\n",
        "    # Add up all the losses\n",
        "    l = np.sum(styles_l) + np.sum(contents_l) + tv_l\n",
        "    return contents_l, styles_l, tv_l, l"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "czPQCJhY6lud",
        "colab_type": "text"
      },
      "source": [
        "## Create and Initialize the Composite Image\n",
        "\n",
        "In style transfer, the composite image is the only variable that needs to be updated. Next, we define the `get_inits` function. This function creates an optimizer and sets the parameters to be updated as `X`. The Gram matrix for the various style layers of the style image, `styles_Y_gram`, is computed prior to training."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "attributes": {
          "classes": [],
          "id": "",
          "n": "15"
        },
        "id": "xh48eP-N6lui",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def get_inits(X, device, lr, styles_Y):\n",
        "    optimizer = optim.Adam([X.requires_grad_()], lr=lr)\n",
        "    styles_Y_gram = [gram(Y) for Y in styles_Y]\n",
        "    return X, styles_Y_gram, optimizer"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FpF5u_uX6luw",
        "colab_type": "text"
      },
      "source": [
        "## Training\n",
        "\n",
        "During model training, we constantly extract the content and style features of the composite image and calculate the loss function. Also, we use `StepLR` to perform weight decay."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "attributes": {
          "classes": [],
          "id": "",
          "n": "16"
        },
        "id": "76bO2YR26luy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def train(X, contents_Y, styles_Y, device, lr, num_epochs, lr_decay_epoch):\n",
        "    X, styles_Y_gram, optimizer = get_inits(X, device, lr, styles_Y)\n",
        "    scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=lr_decay_epoch, gamma=0.1)\n",
        "    \n",
        "    for epoch in range(1, num_epochs+1):\n",
        "        optimizer.zero_grad()\n",
        "        contents_Y_hat, styles_Y_hat = extract_features(X, content_layers, style_layers)\n",
        "        contents_l, styles_l, tv_l, l = compute_loss(X, contents_Y_hat, styles_Y_hat, contents_Y, styles_Y_gram)\n",
        "        l.backward(retain_graph = True)\n",
        "        optimizer.step()\n",
        "        scheduler.step()\n",
        "        \n",
        "    return X"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kojDlcFv6lu2",
        "colab_type": "text"
      },
      "source": [
        "Next, we start to train the model. First, we set the height and width of the content and style images to 150 by 225 pixels. We use the content image to initialize the composite image."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "attributes": {
          "classes": [],
          "id": "",
          "n": "17"
        },
        "id": "bvrto0O06lu3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "device, image_shape = d2l.try_gpu(), (150, 225)\n",
        "net.to(device)\n",
        "content_X, contents_Y = get_contents(image_shape, device)\n",
        "_, styles_Y = get_styles(image_shape, device)\n",
        "output = train(content_X, contents_Y, styles_Y, device, 0.01, 500, 200)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2uBbwpSBDstd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 258
        },
        "outputId": "5d6ea47d-56d8-42e9-eb9f-fd14fe7255b8"
      },
      "source": [
        "d2l.plt.imshow(np.asarray(postprocess(output)))"
      ],
      "execution_count": 224,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fe3517315f8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 224
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 252x180 with 1 Axes>"
            ],
            "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Created with matplotlib (https://matplotlib.org/) -->\n<svg height=\"164.778125pt\" version=\"1.1\" viewBox=\"0 0 239.2875 164.778125\" width=\"239.2875pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n <defs>\n  <style type=\"text/css\">\n*{stroke-linecap:butt;stroke-linejoin:round;}\n  </style>\n </defs>\n <g id=\"figure_1\">\n  <g id=\"patch_1\">\n   <path d=\"M 0 164.778125 \nL 239.2875 164.778125 \nL 239.2875 0 \nL 0 0 \nz\n\" style=\"fill:none;\"/>\n  </g>\n  <g id=\"axes_1\">\n   <g id=\"patch_2\">\n    <path d=\"M 33.2875 140.9 \nL 228.5875 140.9 \nL 228.5875 10.7 \nL 33.2875 10.7 \nz\n\" style=\"fill:#ffffff;\"/>\n   </g>\n   <g clip-path=\"url(#pc70940b4e1)\">\n    <image height=\"131\" id=\"image488d14de91\" transform=\"scale(1 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2KIojlChIV7PzOhwrMRZNwsmDITeNNatiAwz4mJL1/AkQkQrXGBSELKWKFY3z8R83su7tCXAchqwCfkn+MRNRotW/FSY8zqHWZMrLdmoeLgLJXTjwjMfdF8I8F8M4QfQIDTqxEGRzCmJtdr4ZA3rHwKbJLuAZ8m7XhZPud/ap+3MoJ2SVDbEMZi2jtinALEkIdsXAIgY9RqjqIfEr8zfVnEIGizJHioFX36hGCujfUTTXjAq6mnnwJjKQasFsxo6HaIOxu5m9ZZaS+TAbjNwbGVWBVImlUkCGU4FUpSRAcqf0XBa+40IYg+9hWLjOnXwWhi5YFAgWyOfDkQ2FlSkq/9zBYgGrDukEgjATR2TXmurQesYQVzTulFiqIISOBjebmTd1lWqmRHbMLC2LEWB5oJYhHsDtLag0LQGiDWr725ykRqUFikQIi1GETug9JiyznujD9jPsGWN1MY4serBch9tNwBZlPB0COga24RYMj0GVI3RnQr64vw2eIR8a3p63sTMR+Ib8phVYVYv9TB6fJ3f5Z+c/3oXkAKjCQR0WPNkSN8NQfY9FeKodVxC0APb5sBmCqtmPOuxiARX2OAbArT3NQobjmdoUCRwaFcFdyyU/IFMBBpbIO/3QhJ1lIQxZSxS5D18BVEoYln0o27rG4s9qMm0mz/AjVfJ5EfQuaANupQFHRMLdUryTBJClYJeFeITw9Cj8aumWwEnBXjmmDE3VmT4SDsYiEmBkUI08EXWhV7iJrsegmGFadUUokjXMV+gCRxqwJRIxTLKzVkDSeRS4DowFUY6bmR+QoIF3yn1XWkwmYi1CwYoX4hPm7DYN4eq+TLnJe1Sd9zj3EMDjvP6kPDOXUeH1kSh3QKsO3kOwlM8sFXYfGSLyMZisw2QPHDxQawLzRwB66KsuMeAoS0S+uwYEys33CjCbHYh8orUQNFflomNowNIwT+WhCyWFMo1CmpktQcGa8sAqfAjlzRBJYoWbd4uOhiamog4MQugZbvTAqp+H6GRokaTpPUE+6bjwNUy3sonIK34qtbFZ1+27mZGk9Gg290NxKpMOYmpyG4LrOf+qytkrn0Et/VOzxdeRAlvglrhmNRurJmcl7MBEi8mctjcVJ1e4GKuQNECNQI2BZKsAjCV8WOMxicHLHDS9RJDWm4Fgm6Rn1jgjyqGuIwiiVSmGAMY3YbbpQuBAP6Zp5pDpx+/a5E4fEWNdDgYpxH3mKwkPG5lx4MCNzEzmCFxcxLCgGZHvzvrlElVoszXToLVsAL0N9CIgEq7vZfvjidXkQKi6A3C0vG3GpKflU2MerAi6+6BDmKRm+CpUcXDciNMKWg5Q0ew24Xa2xjGQiJ/Am4IEuFnIVInL+qm5Zeow+EyGELMipGlornswSuGkeX5QQvYMJMIgRPpWlg0puRUJoD9JplDfQr4ohXnKYVyOLnQPCyKHLFqWMeEeOkN29sW1AIv+IPO11bNw4txZbEw/Yvg0bVA6WLkeDdhW2YigUKmY1e2qEPyycFQIFllUaWTs4cMyTY8RzVn4Q2L+T0hQN8ExAXKZRNQewkKliKhwhVFMRTBuBbCFzyerPlOnRQESyoQFhJsYd66Skcssexs2SxvXzACS4dMrGkg5vVYA+4pyuaRdLm/XgAUJf7DlQJAAQYJaBZ2FwT9YhV1I9oF9sI+ZqVhI6OuiSBYgVD7Di8lhnEiBrUQcRzmbtWQHHGCg7xvVN+NBN7JkAVY6JYEIDRAz+CaHWsK008schArQMyi4cQUcCxYb4M/JdQGvXL+5kz3m4BHTokse3NFsyQfX3G/2QSEOtBxjTVsFbiQjedaC0HdRvoYoxjG3W4CVh7QKILqkWDiQToctHgYvCTTqMBjpe+4ZpmBbFfLI/MT8q+hSxKI0xzvUs84WUTMPp+AmIyJVkxFqiMCalAC438F2igm0EFFoBXhPC4Q8bLoZc67XRbyw4P+mz/LiPFh7hBgTaSj9W7YZQSHC8hiuJHANaJRN3crImUBYg/2Hnpfpvb8jXq0EfFv27I6WxaUaiOF2KPNVWIXH14PJFdN3nEKvwNLkNswfL9bevwRqCWh7ueKKp2k2JB8BkI3YNn3vGIWCEW5GQqqAbWIdXaDuCIwHMVWg63v0safj8yPwdZRPJB2ZiZdF9sT8pwwxiuReJ9upGDmcDQPE7B6MWI5EDFgVcvePkxo8r5ITHgUiFYDokRH0DEXPmgWcxhwmBTcrwKwi7LiQRPco+f0d81SmgA4F4YpMiPvrWvWaAUjLrGcXwCFwHEH+UVqMNUnESllitWquPUUUsKpgNSnCAPaAEBo92LdtNA6pKqDuNTTL0cBgMaBtqkkB2MByZTzLOJ+BDWz0w0VCwUSiyIG6uJXrcP5JYHghX/Q+iP0uqkgnY3D6QCCiSjU10tUT52F5heFlCKL3wRMpuRfpwkMfAals7rEQakGEGkbANh4wU4N4Ht6qLTC2jotosZFATyTi0+ehOXMHQ1SND8ljAHTeh5WmxjZ+GQFuAk5AXWlINV0js4buCrTBcxQMyR13tmOkju4NUBBzkov4aDULnpB9O97gx7icw/lNmmyHxyAWimYbyucae7vGRNiGUPdcczAc5uehcgQc18KxlRjWJpDNvLMJ6ztQq2ratT0qRkRsHpuHqg34WLhUE1ApeGFiBHmgrtC4rAMD40H25oDBjhh8+rCRcRalhHEaSeW2ES9U06BmR+bjtyDPLgoLItyehwY92BU8HDRnAJ+u0X1srWHcEfd3khYqWh1wXAIDR8d4r9HksXn3NgtB6kzfsoqHtAUnfGAEt0eAl8kTdnGTaQ7gMuEcsIMiRs6B5y8QOsBkAaiacBBgeIEnzLNfB+JY099RjMZAorFTWNWQ2kJAWi6i04Qp1TSmbaZwOEaOqpjx6ilwtQMTYSNzS2B92Mxag8TvOsKSSMTziXx9AjhBjxlMODMLOMDCLEV+RGzYaJt7rF5gkCNrGhF8Q0hihetaghzijqA9dthHl3Qho1WaJKKdyEenqlA2VViSmFyGqDwcnboDA7mvksm5GUU3EbUgFp/ZNdjfBWLFgbMI2GQ4Ux/n3edNIdEHBKC3gSjimOfynhI5MRoDrIk6FqyzSBFak89DkRObXOoy+LdguFWfWfYOmNaey2EC5qDNekp05kAhrHAABNSASh3gNgfRXI7STjkbzK8Lgg5G3GMnHnAo+eQosjlDA4SaYfNhNhcTz1sDkiJ38rSxGcfxABzxx+wz4I51Uu79RAW+csikoSWf4DbwbmQM7QrGo32uAmfaipV5ULuaxuA67FzGZ8gzxkn174B2vUX7iMoDGK0xcAmgL9B/DvS8cT6BBAbt+YSvAd3UiG/IHBUifyJi/YNhaVOr4M8Yc91tiKIEtiUuam1R7LGBBShrWvEaGCesX2EygRALy3q0ID5jmCGhF/eAgJWmzdCawwZeD85yEChwFLPZwYv6JZEioEvFHCYX7DaVE8DWwb2JSIeawi7s9/ZsQRR7Zo8806fTKD0TUUMyi9ujDldsWFWzouf0gSTEJ+aYQfdzjSx3p5Cxvmv+Cn4WtuijGHvmyTovD4otYZwy0PmEyBQ123WY9aHnAp0QZTdYaDZoNwKy/Ex/f5/QHEGHIxR8pycTaCC4PQSS3B9dMrhmJ8MBKuK9VlBwEgMWomxeDomGZBLm2lN9HxHLPFlrnRtcZQ8qn4CPmlUrY7Yby1jHHLhzwmNwFWJrCVZPmLHeN8Drw+YQ3BgnQ+AQjnmA25G4rwnGKyiLrD6i0E1IUeyYWKIxn2IbGH1lIHJnAkSJ5Ac/C2daoEK4sFvn9K4IYjYjprTYEFi2ceMExiYEIxYWfdaH26ESEbByyFhUq2C7oIMyeoTUgW4ayP7MgN+w2OMMDC02d6aHBnILk9k8dC4v2kwGUGkBC5nB/lDLOInZZx9FgE2GKFEEw6FbJNu0EKUj9zP5Brw+7UwGmtIRqiKR186B3gTGzDVAkEOI54Mr0LmTJU3eBhs2OMZJgDhgDJw8a7N9RSbqYKF5iMTse73dnIpWmV+0C2+4MwRV5H82Txj/BIcZeUzuvasDNQ39BIa0GYXVIigyHsM6sJixtOywjHGzexoQbZnIDwWcUei+z8W7IWeB5Q5YmXHJEQvnw6nmPS5QP30K+JYolGYNs0YBebYKSw/N8+3NwoTRwoI0xU6ymBwLDXxvDIMA3FK48KYJBGOrKpxBVcQhV50OyBl9E3A8CBNIBDUbtlhLGhJCBpbiWlrnuKVpA3c7cijq8wpCRXcgCtyLAHMmmngMlXmozog1msyqMQepB7qicKotohAmPbPZwx18jLGtBokxxY6xeNsURFAtmF1FiF4A6EOx4KbZCohg2IN6zWZPtSVQMVPYtfD3OWNQIRWdhr7EV2KGOMBhcwwngEnLItAuRArBiAqOYT2XGHEUWLTk39sgSTDMwcdmgIeFPewNCoOAlXK+kVKJRqjtlGevvIUNvMmI5Qxl92bYYIn5ZZvM2rCZSMwaYCoY6GLNOpoyTWnnHDjnUP5tiK/mJlBn1wjYVRtUTyiflTkyKlDDZ0iD4Ri8igSR5UevL6c5yuznKjWRCCngkVRgf9sQhz5YpwEaXEkHPCDhPNPNgTW2GLIlR6xqMUxTGEpU7eMV6G5dZ+XIPC+twg3jVAbgLnwIge3NVIxDEyyo2fgnwbdEa5pF5iMijkf6pg1PIorGgbmB15nlHCz4cKeKkPuRINptBLAp4Cvibc21A2iel2n0UPSuyecGQAfmVsW0GK3Y7CdGp1EmGN9xZeEzSKKv7YIjoua4DtxNaLpmP0wqZeZsT5BsN7OUckAqqAb7Japs+wG2YdBREsM26KpDsCLdDvfAmQO/4cMA7sQY5doCXyJ1h/iSsOIZ2+gYJknfhLj4ZAkGW4Bj1TjSgvEJ4NJb+TyOZtOcVewaD+zcEcCtTZlS9zs9VqmxZA/yjJwmIQcgFTwOvsN/5cHr1Z9goyahI4/UItaBG1vAcysCoG9LruyUdTUWB+XYBnwflY8ATpyI66OfWDAUzuB5IEJEiIPEWOYK95SZ9pBLM4t9V4hdPbs5Ad3XJG8jWBJV5MQmGIWjlkeeuCT415Hw3BkjByzD/gX4Wqx5sTbh7s4beO6Twp1GMO4GzCG6qxvB9QhCCznZZ30GGvy2T+aSVABbDwEV7kaXWL4IoKk/Cn4FIGL1Uc2FS2rKlp6OYP8AGsfrVDNR4zkLviSOq0oLumVvLPD8WVgzMtEGwPJx7sno51geqqrsyqHoB4ugamPi1ADoY5HSAjwWHwmIBxPSstrlms3QmQ8eibF3TOhPyn3LVcTr4LQRGdYCaiYFrabFXHy4LQA0xbyEee68HJb4TVgPQtpLC4RLL2G//FVshCnPKYh7QNvselfWqo1JXSLJZ5mEY3nCJDRVfPAJpyzLL3CJf5vkogpHgNsakteKBSYUqAnGCGaZC5lH/4YB+fG5WQ2gqXF7X0j/gx7MtIFk2+yCxW1VFdy1JDLcdwIcN0VZoDNSFBs7mA1ijjPmxPlCMtgHWPHphUdIL8huPWvmpNgADlj9ENykAit/hduAVk+RIiLDyvXf4PR4CPuw34f3tRmr4sshaphSHd4+9UZHsDiA0ZwCq8I+57n2bQV1yWt48o2b+I1dlGPMep+RxzXnubBTRb8KcadQwFwP2vMHPPFgLz/vw+uiaTXm4Ou7dSMLwacfNT5vR+TrXT7FxCCbMqNYTfnLSKwYgRHPrnQg7afAiJYtc7ujgUA+V5D0Egh4YPA+dXMgBuok2oOLvgdHJEIqvXhIhE+BWHPiOfk6NkWrThLRtGCMSwL0abGRmHI4txMz1wrUy0kYTVp9AJcQWHcUt3Domv1yeyGSrhnQDO7wNMZLXXkAzs6gbYgLtxxoc2ADxTiFPRaABj9zGtZe/tsw3pTMOeB35x6k1/wrOV4t+Hew03fYuhByG+hEAB5Oy6flQrpvLEULRTYlFixuXILdPIhQ5JZxSSt7P5T9f3amBvYEEjhmjelY5eAvk7SrQbVr6AhxssTg2aIwpnERy59YAAAgAElEQVRuNs/T9TTAgs1k+Xl4Bvh/b/JKGnEeaG1t5Ba/laroorOsMAq0UDkf0Odh8pp0VEOkGdvs8czPE6DglIWXfIE6cIyBceM2uZGCJkAlwAOi9yy8AQyKwmF8HXjBiMP4MBeKXddYIaIZgDoOf44jEfM57EYhPFgO9roOHIXoqDEi+kJw8lz1DjBUxnQyNrJL8XjOKaqUvKwKtGaiwC1jeWbQ8EH5sFqRWDmA8cOwl1eaE1JaywNtU7EmJ8K8LQ6QCC8B71EjHreOQaMDeyaHOUAhGgSEpVzLZTTL5vgFwDriLNIcUt8XIJr0oJ6FSKQSqAeAg1Ji2NECYn4XxX/9kd+g/rWfB+AbrDKpfUbW7cj+3aHFeB0xTwN9sbfiOE6Ot3EnFp0pC8PRRnnIw7+9jLUkxQ6YWKFbu3C8Qu6km3FhWDEBnDZFvsq62Qwq3BwCQzj34UXp7vImpJn/qGY4dbMIUz4vQ18FHj+GWBE0eMwxNjqGr5iamzLKPxS/DwFOLzEZg1JtLOdzEP+GjGaUGI8YWMdlheSVPQkCyxdc8oJOYN/VKFy0N1vk3s4CzYh6azu31pLC2BVlupcgfL8PWA7MglqWOQa+D/V5GIOdGfI7oCex6FKuKAC+LdKkcYbL/JXswZ37VSMLFZd4CMuT+khVI08szJXtJRbpk9B5M5eiJJ/qPlIQTFmC89z0feBoC9yJhIPaq5kI58GtNeCOySEAwoEZKCELKhmiwfLYsMGvePhA1QLGPSSxYjreSQGhnR0WqcjwL4Cf5SxXgYu1vzx1/wKwnsI7eT54IRcmnZCxnl7s4FIRbru85OdCrJOtSYHYXKtiJnIB627A+mTC7ETMhTpAKFUdQ2iMSekA4hhibJiZgx5cfh0eOFOUOTTRyJAchTvQXtLG3k+Rgud50KkbG39m7pWWrcd93oIvFMlgT4zhAgG7bkWSxKrt/BnltCDehm8Dn5XeZtljY3sPO5e5y54YUGEEr/RxP/Ytziz+JOPFo3KYdyco9nANng3qsGQG8oBIjwjvAIzAgoVTS3msQpACzkk8TBhEVqXiFEyuOYI33lnQ6+RRch5FqIYhk89W4LuG69ebArO7ScHZV0+ZD8G9CsJJBYNjcHUH0qEpxFa+LYVhalHN8jMN5QWIeAxZ2QhmhUBlx20KzdREOvUnEG/hm/5zXLSl0MJTTOd7E8Xyzz8cSy1eyZHEYnMT+NcnXyC8WdxRtqXd3tyV2lSluCoAHU+EfYsBzMS7F+2NrZkipzoM0+n0wvJBnQF6IpfoTEGLga6UgblfLUIfhJLG0A+MeTyByveAU3C0BhYeK78vlONmCzhRWEtutBrwkb8jfV0Hrg0BLdULACoPwk+lVP+P1/IiIbeBO5HmZKstx6gDmcmlewOan5me31OTCY+a77dM/jCYIh87fY7NA0dg34p4D/DKru54AwZ7pA2L+KTGWdcChx45diy0gTmrJCJ8Es8XxPVAwk0MnP3j0M2coMoHG+JhxgoyOSijZpqHZuWbzsAfk/t7cmXzA7xtjRrML8CkJsVv8jBPDQqHCQ5xuoTPLF7q0aiKUWFgOt3vX81DrzPocQDoO+Qs29uHmb50Gow504PLeTCanP3XJiFs3MAm4ZyOWI7FEvE6TWhJmZqzq5g+ewasMxAWNQQWOcCZgDsZ0UaI682NGzJGSeghnRQkL5EMSYiKagxAOSqqxFe3qcUtLHMxOyNVBZLVfaiA01TT+I7iCdPv/2NEnRDglC0ReVHMzI0eQ8tshqGStTEiio5M/zVI1Auy40vbsHgFbvbzee/yGD6PcfyFFeiLnnD2FnALbmBxjAU5lKZaHJVyvVgjq3zsplSzOoBtUzZnngTQopP/uNymuMZcUmV/gJyv0WUgZOYs0Kyy3TV6VAIoqbDx9Fm4aUkll2EK8DhUGgwdmPc0DLXociXC06pCZ5ylXcIgzR0+SAwEJiI0wkMKo0VIsEgW5XriwUNlJ8vbkyFwDZY1UPPJhMXskpVbQx4AhnhE1CKZ5xyFe4OeGXEEEoADdVNKrgNseBijQcpKxRDjoQbbeD2nrJRSye8GMsaFbh9mX4c7cOEKPPWxx8gdBDfvcPOVixgvKZgVHD/YYwZo2jCiKv4vbeJZ0lIEclTO90HS65zM2VgUepUDMcX3BDE9xN7rIJ0qsy4hPgnVeeCsUUZfSWTv4h40PF70GnzHBOCx7MF5SC+aWGJrTJnR+fTgwvvFNJ47nLEOnKjBMEumWWPDgY2HzvHA8CgMPw8n4KEvwnv/87fgpz4LXob6J/BqWZngNdm2gQbO8WZrg/gA1BxYmSX0JLCmDhVcvkDrVhGgvvoQonOm84wYw0qT2opG7Rk2YmA5TlzgGBz5L6C9Agps3pAUxGEggWleAfqWK2du0BHEC4F43wFmabd2wIa6DkxETCWPYDZWTpbKm411eFPznApn5nRuoj6agjLhD9/TxpDwkg1fNQ9NkAwpXgUVoKwITBJXIeciWBQbnaJWSO83oGR1HFBUEoNuH+ymKVOZws08LiiTZUO673wzT5m+bs6hnLdURJUUbmWPZWWgBhBLRKTcXfckRGu0m0dEO1PPzOa0GEB9+GgBuau9OXHHIvhRDWQ9syCeVBPgFIZD4TInDLPZsuCGRJ0qbOPaFOOMPmpWMRqTXjEH8dGn4PlflmeDP2Pl3d9l/qL4xwAu/cKL3PJ+zXwb5Gb7IAbVMBGKQP/zL0PnK7K29wc8o94mevqvc6e5iFoW+LR2/yFukoC9JkXTfG8qJmvuyuUSlJbY5UeI5z8CvuRkiH1Iw83fMvEvMJx3CWZ+KU/+V3/+v5s0LzEpZhmNjtOEH/5voLUCFiyxZZTIbM87WbQbJBuwr2EEf7qT0CtFWj72NCxgStopsereTx+2wYR4C7mL8SVr3jorJ9+kS87pW7S1xu4BwQEdIDBJWSlX7tOzXBua8hpdIPQs+Q1Ff0/6tYC5GXLlKga43DcOWCPHTQTY3QjsNsAY3dMcdHXhnolgIbjEjPGU32CVCQ3GiJ6qdNnlHOVBizo/oKKXaRMYiQafKE+pEPexhTY8VVwCImc5U1JTFvpb0m1ywB8zMV7bQ7leZn2tFNW2UIGBVhSDI/dXRhGTkfSZngMug37nDaLnTxu33y3a55+E9ve4+aowA+vf/CnO5kek77/1R7jYWEC9VZQ4n2SQaxWwngfS4Evcbf6imB9nQM/9PFz8p2aiHt/7irn3hNz/Q50R7MA7idCwhUebDEPJI6YmBqOmp2D+b1IJfhU4ID6AYA4JSe5/s4BDzLTckgw49davwnO/RFw9K4y+YhVONK8toaFSxgplTOBrtjf1Pg6ALjWauTZ+eHOy8Q7v23molV82IPfo4W1Gw12apVyxyIh9iH+WzuHuTRf9ALY0pZRAEZoUYGk42BvS3APqmQvLEoGgFJUwjGIT0j2ACexlKpNyCc3+hsBuapgUyghlHhHzMqQ2plA1Quly8oWaNhaYUxZnqZCHfMlAqZgaOBcTMTll9XJjjC9LS4ano/L8fmA43d/HTFjKn5iOIbcmuX1wxhFPmVv3Ad0AnoKtN7Jsulngx9nnCPsrwNnf5d3Nb3MO0aFFsPpLdPn3xlPu4dYcIh1jU1SdX7OBNhzvANFNNkx1fkaAB7dnngXX2E5ngN7bgpQa2BE3wDspeGbXhyFYJcmtHwKOzZHAlyhwIA3fBqBKKmbZLQOkGYp4c62hE8Of/H2cDz9AdOqvyu9ZhbPMOH5IroaU2SU55cfLSUFZvHgSc9+gvGxYZcHMh6Vi3UFihoiBazDp0kgH+OTxk4BUDMFMPe85ID+sgAA1cw4Yd252UdkZiOsM44HcawxgVlmC88iV+XTLjKUgdWCclS8FrscPTK+J+x9QoMhmSvT0A4ebOfDo6ctTKaTprqy5WUEOky2KVCpRxsQ+7Lhw6tZOTon3oOAiCRJ/05REH90DNWPhnpBSles34IE1k0IayXpPnIA7V16Dsz8Bdgx8SJDnU8+QWjD558/iHwNvBcLb4D9m3q2gU+az46/B4YvFSk4ggrHxvtYA4giCdSPXzYOGyscHcLeJvhERofkSHj8OQnlQPBUC+g5f8FbE1ZoV/AL2rn+G2b1/Jhl4SWR+vguDRLAre5HSAlNJGOIDusoV35iTci9gqZUDj5KYo0C1LacyHMHW2Lw0ZQbA4YHp6j5FtL4lTrVJ/raQGEiws/KaFdjcDYhMOnlWBme/e8hs0mOa0pZUhnub+GHi0aFbyhxTF91krZMNoU1hOsByPFO2sBw2U0bf+71K5T+gZUxN3+PKAg5Fu3pRTJwlMzeYYsFHTVonOwZwDwLMCefqmG4XJHojJGRIyJkZhz4x392BU6fAichNtd71CfFngZ2G+OjsxXxmnc99nuAffJaslA6NDdq2oNV4XPCoFySwg+8OAI7z3md+nTvMmASdEXCVyv6OzDvT499+P38+AtLZNb6eAAdZ8UE5ZJ9OVvgz/jMoRzWfqtG3fwa2b1IzdGVNmew4g+DrsQAbH2bMkpzcpxCR7XK+xZGhx1HMnQshMVCtY4og7CJVHYW7XXpdbj25BilDrGMQqDqJa4PbAmw05nCMRd20SSmyNWwyLXU9CwTsIqy2ZDa+n1jx/ZqvYizdQx+YOL8s5XQo3WuDhGOYqnCRDVPQhhqpiVSeThDt514pVX4vWenPX9QOn+XyY1oXJ9/JKOrizphkE25N/Kkyf+XeFEBEHhu4Y0q26Hab0UgBNXjBeIeu7bPBK6w968A7MddvwtIq4Ej0+M9s/Aqb/1Of8f/41+hdNB6FKuBCxAtQX4H+HZ786Efoa3iDCGoOVcOGbRCrwd0up4Bd/23W278mQM5g2X2PM+9/jXPAK91v4PMoz9ozUkbJBvpmUwyeTENMU2OPw4F4eh7UzAnCaHyo2PZtiGNqwQqjDtLhojHaZzcqgETGNNigEhOLbQ6YAzQyTzY90JoGXY6q85LPBBw5R45FCRXKyDMCiDsIKvZzmlYcwwRMlUAdIyT6bgldPpALUBREMLDKvCT2+FDxhwnoJGJoRMJ+hvlrEEYYF1EWezNVjdZYAUEnGbtNmXpB3g/a/oKDYinROw6ffeEQCjEl3pYfs/RojLk6BbYrFZ5fg9aDE/iWMIWd1yO5wRAg/4xC18BpfpxoGZJvf4j1ye9Te8aCDdjv3uBMVEhbjdt/l/QXvsji//CqFOGNJlgOjGKIfu4f8d1/8tOcVovYSqJE7nKIeK0eg1fFujO3Bhct2LeMe8qDhUd+Fl7/P2F9iEOKHb/FhBo+p2ChAT+kpPp3yc4amRq3As+Qe/wvFdAhqCXDXCfIBivysxNug3cM0jQhtjyuqRYJH853KQYSk6teIf+ZxgkKnq2nX2pQUR3OHfOAGrtjxUQdx+TZSi2pcY9SzQ9ggiqd2ExdCXqiGOdevE5pAmBEYCW4WjYQhFB+z5aPECRLA3rTbEyLNIBg5BCZrqhSKCVrZBmcRatDHoyWFjrJ4faB+K2+38UPuMecP8t0nE5dOlLXAHMtYHOGSTIni4/IvdMAP/TXPg3nJH/6PL8J34GUJgQud77d5m0ULNeQJK6fFxy7cRf0d2BpBq/Swp5xsZpKCN/l3+EvLxsc+qJxY/zOFbQ7a05MRGUBuJzih69IyITlc+BAfAfYiVB8GZsLnATGb8DrP/Y5Sd2x5guw7r7PA7/9Odn3WQnE22GexGh8C3MRzt5dVjtaNuzRh0nwCNzHuGk183dyVIMAJxlQU6BOgJ0k2MDJ/jW8g5dhT87VIDSOxzVL4nOceXrtHyVYWstDMFQEaqsP7NFsAvSwxiPQE0P5D8iztcwWBPE8V0aA1YLqjESj3tEQXoagkKuNxZXFOXlwvXyuPAvbL5BB3e3gYyw+Ri4cYhFh5Pc22FkQ3CXDeKDIvUaMlyEa8U2L2BanhV244kEyLFK20yxUR9zjphMl7+koac5ax2hlgrimXimpD/0ttdQtLmWXDYFxontvz4hYuQaVk5fBGZXEB8cQgxIra/AYWGJ73at9BvcTwDtbuKP3gH8tNw1jeNsBXqOJmKeaLhwcTOCBNtAm7QPEqPlZvhB3+WHgyMfAuwKzv3UWfuGLJI3HhJ1nlNv5IeCNaW6+6KL1p4l7L0H4v1E5PeHoAC4gtVUBrANDgB6G1obAyMG8D6RnfCrOAAeoMeJh4LIL+zzBXep0zIier6XsJhRBibaIeQHXOCVL4oCMqHYk4PEUJEhAHzcjtF/WH/bE2Vl6RVJRit3Dwi7K4pSeAin+kQLEIyOD3Nv0X0A6024huVsAIVL31S6smroD8fXpKdybSqEYotBZWQBVEauPBkhJ0tg8eojyq9I/kGC4ktgqRdC08X1NmZSmJ0TpcvZ8WvotA8H3EwVL1x0ODNCXSrPJwj5LClD9kVOAeLvGgO2D9cxR+DNbMKEG1Eunvpn92aU5DIleuwHP/gwxLndxsf/um+z+49/n8+/8LZ7fjXhgFk6/CJ39twkrj91Hv1uTOKLA9O1ZoqTN1Ri8D3UX5t//TdZm/zoH1KQCVyYy3CJPjUUFnHYgnIWb5tCNsh0+K0rhFVyGFNQ2c69UKV4k1MdUNa0/wpnOd4CQY589wqX/S+cghMz6c1W+DIs1ZZHO24mJ+8ngZsrx2La8LqCuAM+jG0AnRGSUTMsPtjAFmaabC9uDwueXv4lJp0zuQDoJwGHqfRW5n+CeMyQTyzzj5eOX/ZaXNLwP0kWH+lNg2JO8IusvRNTyg/eIPeree+73N0sIylqpn1STJyABOG3GIrYMIGKG60kXrFlsUzwrs3LZSNKVdTgg8Q7U8/x6La4FExocb8Lo+j4zgBNo+MN/jP7oC7hPP8U2EJ34SXhnnre2f45kHR55EdpMuFsiJkpRyNU7ZibzSqZlfr+1bcLGg3d4nr/JZf6GOPD6RgL5zE+h3v13ko6lNUQbKO8os0+Afln62PqROluezDvEF2BGGKIgSl0MTExIjm0IVRjBnx49y3NrYrC3CAAbdRo0DzJM5tnvBcC71Kw6mfA+MODay9i1Tc4RHQAflnEZusAEbmUpBy1KjtP7FFy9T8xaGkupXoZZWKD5PZvHfZhMhvwRmRalOBwZTP77dLOANDW8xMrmYR8yXGRu5A/sRtoPSuHvN6Uyx/iAgMf8frOfDoq89tG6PcBL4P1hl5RFrGrVvDvrAIkQNA9mClkI8bf+CUIBG8AMzC1ACOOrGtINkekjMc0fS4A/+iaVP3Lp/Zs9zpzxuRqAPvPbvLP+X7Lwm/DJX/+ntIc/xyB7gUsJEAF19q4GYsI0VC8FujfhjWTM44ugdzR/afF1vszTDAwsvpae5kMP/SScfRXe2ZIQ/WAAr2UAce8FeIgJxrlBVYrFQpCIR/GMd6hc6XEuXrlM/SJ4NQfac4DiFssMs44nQKVLRmEmB0p0sAwZS/nQsQXU4G08WADdjT5A17y/SJRQEms2goJKq2JpuIpQspOlZVwkKlzKhXhkLFnqUOrrB0hkihjLxDxplFRdcw49F9sFC54a0TTrPpc/6FCUET+7zyo9kONRYaq9x9FnmtPHhCesAv0+M8D5DrzX7ZOGYM22+HQT7Df+PdaP/A3zwolsYf898FWow6mZARMGbF3dROp3Pwm4sHQU1ApphkHvIykD7/4WyeinZZ//zkfhFy/Q/+fPMfzmAeefHNCftbgzC+OSOXq9WiEigLclNAcS0htfy69L3P512HmDhxafzvE90PBNvcgpXmL1XATOl3CBpR70uya34BZwEuxRhMtVonveADqU98oGoXAqvwYkxNwtrCZZBQPMSx3Nm0K0JSUh05vTVHGUO5mc/DVi2SsUozHimVwEl9vkbzfZPSDcPQBGJMMo29+Ca4QQHYB7BFg/lBhgmpytGtDOpWJFgqugUtmCICjhToMsyVofyiPIqpFMlSW6Xzv8e86Rvj/Z/wFdDPf6TbJ8+gzcOXcwcldJ/Do8hrNrwx8jyueJLFa2BQ8peK8DaVfKg8yxD/E2tbklWUb2ks5fhvG/LDr0AI+UFq9zg4dIlajtu8yzyyqNB+GB969wdNTh1o0/4Ojf++9g+QRj4Lv8PX6E22Q2CYXxNWRws4GarEYPUhRXsObAexDYlPCDsxg/yM5XOVc5z9WaeG43VZsbLwOs85/PHSeLk6xnUsAE1CYws0XxOlbz2qE60DO1V8VfjJpKcdGoWbMrRepCPu8YmNiw1c8WlF3Los3K5D8gr+f4iOxkuqdRug/EhFFcwIJ7N7RE4D+42ZBbB0riUqJhzApTyiMgFb49MbbEpRFt617Olek3mY/jcCvrKvccIIOp5WIV2XCWJf19P9Enn5cqxvhBWulAOSYtFhblXYS2A5We0IRnW6DsOgkzsPgiC/6yiBFZSEPt19lt/Drrv7PDmz/2Ag+elOBKH7D68Hj/Pe5ebrPLiAiPgA7BPFyvwaO1L/DQLnB1if2nfhWArTOn+dv/7IBf/qzDgy4QDrAmY0P97CJNzsBqhgDF+zwXCS/4PX6YPzNY+DjQ3TugtnfA+OiDpdXXSHcGWHQhSHFjwKvmtRNj5aF8YAitGYku9bBl8+YUmpNF6IxJhbAU1DJEv21qNp06lr9frfzujimZuHzQ66Xv3R4ODvHmdRNVr4EUTUpqmIo7RyG2lcW3zE93gJQQiXN3nLE8zQEOhBCSYJsrsnjRNywaU4gn5vfUIHhJDHEQMbp8CG0klMdOUcpYK7M1B6XPFihXoae0blUkimcyTeb5T5QEzulETq6iQPwMoV0KgnNIL4o1UE4jnXY+5M1hYML26jDvInIy93KhVe5ttQq0noEb/8trwFlYBh8T420jCSkL0N7q0OHPwTtJRwH/H23vHWtbdh72/dYup99ze3m9cuZNZdVQMywiKcmixMiWkASSElqJDSe2EkMJHNkOHCSwE8dwFCMuCoQAVgBDhmABiaQoouQ4lGhRbKLEMuTUN8PX++33nn52Wfnj+9bea59735D6Iwu47917zi5rfetbXy8rMrGOgc5v/EM2mnv0r/yPrDz3BM/9yhMwslxlwtm5jtgCe0PgDvN9O9OD+3YJiAU47AG1OdIUvqaKcBewd68KUq/DcogUbx7C8yFkfSSvQdJ2yTpzPEDk164rr5Lm5LPycyBNRBorstwMyYHpajBeN73LNFxmUxPPx+VtRSP1BLVCzehKhcNi20tpAywW2hoe4eFCMqtb22Ibvbgdg9fR2mUbc+z4XqiwG5qLo7Hj8soACOrY6eTo9e7ZLaoHzE02zYtI6dwgvpypF0qQ6XWOu6RQ2IrJBemPCz3xD8M7cI6QhfDvgoG2YSmXCUUTCNUbHwYN1qhzfrfD2pvfYO49LzCnbY7DUExnW1/9Dmd+8YO88cU1Pv/yHrudFerUiZkjzwI4H9FIoZbsc8CAqNujn++x3oSlBGqvfp1aPqH7we+XUkwdIIjYzzLatAnrOeRbDMcjdidTsHsYs089HEE9Ynf+Io9WLnPrYV0cPxHCzsfltkdYapElCqcs1HaIBvt0dyxRE5I5hHC+C+x0ja9tLZEFMB+V4o31yLDRdyQjyFquvNQSYbjMXHajaBiyF2wwsDVGQ0tykMNAoo1quim5hVEgG59HpRI7mshhyawSvEA229q8MFvmuTiUUh/xKwgQgw3BhFgb6vwNGTVSAjlc5Edxw+iB83PdnULaBu0ULwfBHcwWRUV1EsoOP7UAO7Ul8jr/gEtDnTE0FUiN1H2WwL9AAK46gTEWE8mf1h0OjD5PgZqXH83q08eOsLww+j5ahdw3mWqybxeCTBtMYOjWTsrBzIFuGdJCakkwLHz6R2EeXv3KL0P6cX3LW8BtSHrwUDvcsYUFxltLsLrGy8kmPxgKxWxMr7r2BcLtWpA269zfBcYQu2rODakuibXkIrdpIWaLaw/my+8VQKjUNY3Wab97nbcuA6MbwDzMrwMxv61l2Btd2H0bVlWUmuYyJ7OIOLGcsr8DZtmwRcR14AmtRkoK6YG+P7WlWJGXuuhBCCPlptbZOAsP8MywngwgAaxHh5G2QmkeFlQyqMgzChTPqBYhEo5D7mRy9HL3RyMoa8c5wA5zJeANRJzTeZlA3h41lJCESMl56yu1uf+o8v8cAi2TM8i9D6Es4ZN61z9ON/f9EY5rHHcwPJEzCnoU1R1CP8JQM5vgXfCJX2D0k89VuE7kJh5A1NUJLolg9dTfeDfNf/pPILsI21/VE3ggBarcTneA8Rpbh5s0gYscYPka1/lAdbKLwANImEMEjNIRlQKZK99uKIM6L1BCYAwP+mhJuAlnn7M8CuBRT8qqtF1WxaDFb7+qiTIbEvPDKOHQkclNrVak1G5HfW2uAkkOsAMbS7LAb70JLrI0BMjUUBQI0atsYunhKuHjb1x6jPONYnkA5PUrgDZHVPnbmJxOMNGAIx0h0M1gWVUzayuV3kO0X4cvhkVwYhHpyaF2hrVIjRtI6z4A5qW8lD+yenm0szlIe54TEtUzVEWpjBzprhtJ1ZJ8Qnmfqj2mcrEDmb4vz0WS0j9NTNk/0B8zGcvSY25bfsyJqGCVDoTvWTZwpQVxh4T+kT55QW45HxjuJPCj/+jP6aePYAjGHT3/pBqgI9UTnlyE4Ty8+RZE925yBlhNrvNQrTyFkcKmPLY3p8cJ/q374wZ8yOtg7zurAsaqDw2Fc6kZ2U70MECZQF0LiwK/KJhWXCTBDuqdBx6CvZ5hlqUD6ZIzVLAgHMERd5/sz1C178XvdOQaY4A56MSMCZW7WIxue2icVyyTUIgIam1YXpaFrjXlqeMY7ECM8xMLWRM5LZqscMKZ2hslXkiukmFyYDnp4JsLPPoJRVmm0BhqjptYeLNuKFPOtgCrCoOtrDHTijaFMS/A44qmEIkCSgJf1VgCbJpXm8rHYFL1bxjKUqqtsFjY4yzHnBfCtcMAACAASURBVFoATKtgUbO1pEPGaIVFLiCE/PQJAcSbv/7NUvnVNxTInSMl5w/P83Jzjcl/9l4ut56VsjUWIGM5e8iOQqKnEbivXhWonyIF+gRALUqPlkk9bhiEvI+A/GsQSIn3CDRL5kz1+gniEXZWDh0ZkM3qiVIySDzX+5MSUFqKBy0TWWCSk1d9eFgryUyg9Vd9BbDSiAAow6keAYOAIwG5jpOndZhEc9QZAxMiY2mF8O4AqKkZ2QA1y011TVckTHXYd3S+Ud1r2qiftdRCNg9EbVnPg7FGcVtx/b8rLWv733I9s00IyQZMdrBJBnFepKWaSKUqa2Fkq5rOrAHiHU2rBpuqTuRdaAJKXSypfEXk25wX19dZB5qPdHPiJs2f/RmeubSMSaThnuCIxZLiu7ybGBbRMoJvvo0JTmJ/7D+EX/1JmfdU2J9snoXEYK/B63/rL3Dity4y3r9Osq/OjRQmNxJ6zPRtWTvFhH0ONQ8+iA84xRDHkc4Fp0XHiaBYVwoTF/awaIjXM2qMYQBLY1iNgcM3uMs2+OLaAZI/O/H9BrC9o9Y9dxB7NfFstoH9hI9tfhs24clWna8w4U4fpMP9orb7NJJznnkapYFuiMe+LR9SrN8fw9azC0BX+zAAK3AZeG5nTw0uCRF3oA+/e3eNuzcpzY4B0IpYbER0a5YgmLA4P50hJIalhk6np4JHDHGjytTGU+ioR7vAI1dcbyC/rnTgR5Q7/++bYJow36iz2IBTHPChffglrb6RDoC9OdJAwD3xqu+0eMQV4KOqAx2M4TNZkzsWrJFSABY1LjjL8ZGDYoTzWAWEL2YpYwoiymrIQKRF2aCA4RWkaBFw4nko9smxYgHYOC+b6RC572HZAO95js7nH8FH5+n9ilxkjJReWgFI93jAk4xZBHuDwy8DT0MqiaKENegNb3ur6xYb7Hy7O2OxyD4C6SQKwCM4LEplyPBTjw1M+Tg3gIttyEb/mocBcEYZwWs9oFPyzQkULUb9EVFqd8aZWeYwXeDR54TNDymf4yXVzI4uWh7I+UEcfXo/YOUMRCbkoZkvCdk2fAc4w15RRBqVnj7yVMLhU/DF/0cyW4GK4Ow3qPVH0+U9z0OmVKhG1cVR96TWbV83BsJ5UVUGnu7/4py2LND3/zi7mAV4ZX+Pt3leio5pmiz9EJJa8eLhcMSQAy4C5+uW4Rh2JkPuRK2q3D/LbWeH8WdZ3lTZVVfCBohsoQzAlPcLCE69gOt40f8mWjWvatawiFO7qX9Y4/odw7M/+hH4UZFQ/tgC9kCUG6sE5SE8tQH8zb9YPOzOHtSNRrIBhckl/YosugfcaJAgyNvzPKZ/BGRvQKOWkNoEdhrO3lToLG6NX7wRQA5fBP7K/KcIm78r382fJFWFMEI3v15aE+GY9svGR5kcwoBf5wme0dkLfOrw/HnYkEmMvnFXnGYBnD9rcC2U7YUOsFiY1eX5pXzW0ucVCngOmBOkTYGHUbgFyCH6qU/Cte/AnQfFI2jE0K1TBFRXxeASy5rz+g5FpCVg9+HY1UAmbLVYV6BsA3Y0cgZJqOVcRfxWNWDD9xc04PQSfHrf8kvc4lbhFYa5ropShUiwwfVhm/+FlL89uVUw6iCGzNNxCynlMVYkoVveoQgCMZNknvpZccw5bIla5GHjeFfNcdRNP5uCM/QX84uNegZzWB8ckKUaqNqiUHJOPvhV4OeLvhNX70OvCRvtkP51LXzGvlhS7yZSQpOE4RAeaekHm1uZgEpacU2knNR5qqLHKOIA34ZfAX7m4x8E47r1eGMMcdMQU2Q1fhfRdaIxRS8IM7tAoYz+4Mdk0W8NYK8vZVRe+jiQ5bypTUNoh4rxPjtxSOq4EJWW3rTmSv3BUzTa2uHo0mVYvgj9O8JNAcJ6HcK6mL9xXTmm5Wt8XUeRQVoYNOT7oa2275qCCZtg4QEjzpsAwc4pT9SnfG3SoRODHcNrjQWW2af5BJi34AkvtTYz8ro3mtK8Rdq1dbg/zvlFLtBtHlQ5g4+oRwIHbbEQJ9rWPWORTTwQGyriY1SJ6M3qnmVnyoc/9Dxceqr4+jhLiHWTs8xEgOpIy94yD1xG5orUS13J/iot4Lb5J+Q0uXcI9w6hUiurD5v3UdkBxjNxNoTAqtrufPOlP1Qx5yQsulyP9yD9nQZAp2wy71bq04DZXt4AnzoDv3vXX7zeeQEB8An4wTPy1UX9bgCcfarNuRMpG2oTeVYjab/u+jsg9RFGzsqyFlPIU3oYYqRNV57C227DtGhsbOAlBAG+mmvHp3OCog+1fL9zAZSSQpkQEDiKGisyjXWNBQxMpTlLCxhrKQPocJMBFzUgxLXVMnWgDt/O58lpsVyDF5FgYwNSeC7ImJgRKzQZzEGRQB7I3txT4mYC2fK0d0zoO2h+fOmsi9TZFytxmjpR1tfTmyXBiIpD14Z7WyfYSrb5oU4Cr0P9Q/6bjovU8jziGUUYSWW8HxYikXg6B9DXAkBhA/b+Z+k12eIj9F/8nEwYmO70EJ6S0FOF2GjAV1EgwWGsO4RFMYa+1pcKIFU7YU+K/ELB9WkCi+8FbowRPSpkpRaz796lFNdQIb7imXkGnvqgVPK/BaS3JuTpBOIOQ5eEDrzLIzYZ0G3D2QVTuqS9PT1NnW2gr5/V60AnLEyJvUm57AXgvBVP9YOa62Uh48dPwApt8kcDfkI96m/rPAdrkD9QS5GaMUcpEEMzKkW/DArRxalKXRVNhnNBKZ64ILxU85S6y8Ay14Amd1ljQMOIR991z71JLEaJ02BUTawD369JVBlwi2e9ys1GejBkEYTS14JkwIKLLB5R7P0tt2G1cq+Me4Y/nL6EKNVBVKJP1HaiQvesQnaRlRrAXdjM4Ty45sXO7usmmmnJkIIozyg1vRE87P4Mg3cDl35MqNLP/0XRjD3trgvUv/IJbP3HeGBcgaoJPaST03zDUEe9vXcQzdzXDvOhvDydsspIzGx2QavAGVb/svpX2tq48OWi9RAv8RoYeOb0AvynV0iBz9wC/kTMybLqSGZ5Ep4qguokVuoc0LpgOQ8s0OMVu8Q4N9hdnafbizGcCZzTSKb/LmB5HtiH3yCoHLwgjiXmd+q4V6OMG3IPpDTBArxQ7PQSMKiIEvNAIzQcnLbU7ymzCSVzM9+3DNrShjtSefxY0dlV9/NGIwwYp23FVATOXXjrIGaDhcKDay1SZ30LOCUfL5yNGWvplijY4xMLlrVteJs3eXj6CpkTD7XJZIG1fbicIy0N5uTzW8B6Mhun+7jhLcKv8gCYn3upawNg8cIVuPLfC/d8G+rPB5IyuWBYVOQzKdIRXofE/HfZsaUQNjwQy08G0LvBzS/cJbv4EiMa8OWrbOz+HZi+zc4Y6H4/K5+E8Lf+WChStsgDPsWdVoNBZKAWwU/AzwE1uw0P4L/7V5KHefH74NIGnK/Bx7gHecb2UqsS+57kS7xGyBvBJYFBG86xA9yDf3OL2t6IH0wfSIbUp+DhiQ8KaG5JI++1BQVer8udXquCJI0QiNUpnu3QRCj3XY0mNZHa5YH8msAsX6NA6IgWTA1p1mBAyN4YumoKpL8vYhRCA36TOdJcud1b8CzwH9XHMu/zglwfCODgRiJWuDWpkgmQ78nBvZXCHXeY0qnQuQcHYASZ9oHxCOLAFt7dJIfhwBanY60JWMuwh3R02QLqsE+XlAhMVCT1NGLxUaxhBaf6D/kpRGFKgIvzDf5B+jHGzlxq3wTu8N8CW3fhf1h8CTKYDAHGUpETuL9nMVgaESwEgM0Y7Uml9YeJp5NnUxhnjK3Q6QGqO8ye9BmppuKY06JvmBflBI2NbFAyLPEsBWINPbh1E8yqBL4bADuE3h6M8mpy4/WJ8Pcsh84qsApD2KPG8lyb/Gc/Bf/bJjkSRbL8DBrpmVD36eY6/PVfWOL33wAYUX8yx5DJalPwq0uDlOjsADxUb/FTkLMMDx8RdMB04Es3hFJzkwp5WQBx2mlnJClzIfXAXCgGmeDEaizvrfRbzCGVFAaCpsAzmIvJ9wBiCeKzsGNETzUNmf0i8FJDaPync/hD4N9Le/xLGkyH8JGfKKr1l40wAVow/0wJgYdDtLy+yG07FtnECEi1WtwJJBzDPWM+INnMKfLPocx2y2Ezg7U60MpgX9KJm8B+2seyUCqnVnwWrS5sJppd0txge/QqLoDg+kDnMxW7Sbv1DNhV/mDrGzxvwDzch9UFmk4SyCyE0DziShbtvw50A5E2cgtpIP0uSjys5BE+dkTEH4azbfZCUzSsgTIXo4dsnJONCmtnE0YMYGugSloG9gD6u5XHo0FGTYD1nBovQXPM6tNU3scnL4rE1YJ3nRzxPEMGA8gYipUjL+Wxy09JBQ+xf/j+/dOUXudvYBA5/zngn2fAq3AzhCUWWGAFS5/JhUVeVfF5PlGRKqEIxnPivgv+dCUqjdvZDLayqBBdjAHT9KwLzkC0UAqbQ2DXxfyYkAP7GP9AA959BVXMh9hcLVFjmdh6gNfdrTpWgUf60N8aUFBGCYpZ5hYQVXcA/Qp27jH049UL0+aEfgJBVLXtLxl4lOxD2hJvnp8RFwvRiIDPm+c47b7KgXSTULW6kYWYNT7Lx7jDJh2gvzeBJRVbU8nHaKl+NxxrEUgrcKoF0gcb4NEuGBNQa0IwLIuRmZiyIvhjRiT7cgbMJQIUtRxbsRKhOAXJQ059uq9eGHtDfrcWbCZhofkADlwfASmu5WTJuWf0iM9YRPtPetavPGA4EYtCBJwobGWwS9O7+SRj4KuCxnzpxnmGFv7aRYiow7D0Ev0Q8PtafxWAcyEwj701gbPrSEqRv7yr5GYyq/sCghuNllLRI0XlznnWtgSyvfK8TqwYIcYpEKktEPIRXNgoqXKOEY+/qxHkvnD9/pwlKLWs92HUQxovg0cE5abfcpNXsG/hanVTHPoj+sLyafJ71RxaKVg8YWiRSiB6Xx+Nlo2RTqrJsDBcpNstcgmYYppJL9IE59yuNsxLRhlxM2QawZtJi7BpxRveg4YzTiTQcOZ+LQiRjIGGtC12sfB1hVtvp7J0gUrdOxQpzOpE5bUtSDYz3buRrGAkkzhAbja9hwT2QF7usmJyldsCyFTOGwEumKk2nzJVWeRJLZvZOeaILlRmFtDLBMYxZWIUwNn0kAfhCQYEcC+UQhqtZSBkBIz24B9/Hf7mkx+G5KacZqWQ68DCs+WzLPDwWwWoeHAQ8gaGp4EezwLTouq5O6xhBM8qJm3KxCvmZnNeD+u1MsBori0vy7fLatdLC8Lf7o6mRwLF3kLiBR47IoTOKIzbTbg4Ghfn7vYBPKg1+SN91+y4UTjrTEWIWJ0HUsvtR5CywrY7gQFEHHDFdMDuMsgHFd9UIbc3gZHIkk11Td7bPENrPZT5jqVg8xZibwgDCNnkUrDGWykMJxm0DMNhG1Mf6Nwt01QNRwMpN5ToZmiul1sKqbIClwltgZWW2GkPh5nmT5TX+9amErTXkOpvH7jEeGeLjrPDTrdgfEh9V/D+wE+QQZsyElT3MkBFgznYOywm3fSgPrs/NaVgvgsqCGNgkRxXkX2vkFsMcDaDN6Zap6hWK2rjjC0ETaG4v3kbfnx5nhpDHA9fAPE91HSyj5REfsXC08AJmd3riHk2YBmZySKRVnpO3URsaZt/npKy3FMYO32iub5Am5SVaZ9zTNgGDuIGX0NqmREhiOT4euA+zGGcwVir07Q1BBpKP4AS8brrKGqBA4H5/7qriDobGYjh2qa8I3dzUDfHmZ2M+Z4Ue7MYRtTEY1YX6N3PM2JOME9KLRtRD1wm5U4ROkQj48G49L1MsUy3Jb+ERpN0LBi4bwdcClycUVW27xHQiEOGaUYTU8WxmWC8wgzfoZIn0gZai3LpAwvzViJoB6nHEcMjr9Z9rAMckmc5m2PXnsnOXFzumOM2sfzp4CzDvW2lTmsB1vzk1gRJ5KI8ALNOcOPSEd1YAlhTuaWHVQHmwIXTOnZap6DUqwrB3zmEU11oEJcRk0VHvhkv4s0hbHUFkjXYdOrPKrSiQJ2zi0qV7lJDrD3t4jFTiRE76AnYMgVQBvu59IOTJ8BBCv0eaguVu12MnMwrFt/kWIB6bw+eWYYjdm21miRjKjFbf0iDzUlw5DDcvAbTCfQnhRoCFIXCuas/PvcHneOcBNgBbGspCabwMvDuU54xI4BbjLjHVdEXFOyJH6UZgCgr21oQfZc5liSu0LjXCsR38h6ndamB/q9t9woHI6jOHWhiis1m0xxYJGa7FtGuQZpalaYthNbLtwXzoZ/+ezrlGsyHJYJOIGJAlGijIj9VsWEIuoYYQZQ5D7eaNbFChSR6SJLS1e+lR8QLAQXxHqHx8H0IJEDw7OghZGLa3QdorgN1kp7hvhUeVVFDsrJDZSuCBTKaOuk7tpCaimtrw13OvfwH1N+6xfjJdYZP/RSEDfoHkA0EYQZDIZ1hA8yaFcRow9nT8OPmLdbp8dzgkDizhSL5WT4oasUGlQSVNIdfv+GknByaCe/1os4bW7BCIBMNvJW5gzmRyqlNpryApWUzGMJ2FEJDuOMPu7ZDaxFxDNMBTH0dJxMIxNhZEV5l6rQaqh6XX6aWoruPeC4bEBniZegEclJSBHaMIZuIDTk1T0lgb77FYnDA00uHpIyxwGZxSHKeUP1+PBTfi2MLw0QXkKZloxVPzCitYfqHMv0asKotxG7rQUsOPYISBdwcAmlVZon6fnz/rrjRazji0oRwLFl/QS4V62IgshhjOVGTFzy5Lrv+QKm28d9xjEafIQ3AJTQZWFeB426AdV6rXCoCdphyKofXBocQr6nN+pgYES/2ZjboSMSugIdEfJ2yAsdfaXzN9SXFFUboXARSeM8WrL4tMsk9u8hXvKyp21OOEUVkVPAsg89khtfehCQxfIdACFoMDGt8c2fICxdgGcuJh9KiG4DAFMS5lknY/O7EqFXF8OrQ8tdzhCusgp0u8aIXniJO3mM6DEUhcYOi5VnZeyQtQ2+OC7/RdcdOJHHCuEp2Q2eKcymzOYCBVkAU6rbsS1n8LbosLAo/vF8ciE3u7wAdyA9VMGlWzRzjqSjwuSJXIZikKkR4UoWL6N6atbLaANMpkcMcU7JHMDGBsct1PYSpKU3yQSQWBhe12Kpb5mOACUsah6apNpycg4cDcFA9cha8uPPKZ+7zE/MwUuNQJ4PJVqF/nALuKYVY4NgijpyOheX3gb63u9cUu4qkpSFwG74Qv5srwTXsYp1XNuEGwCN4wYqV98935UU3OOQb0UKZnWzhq7ToYPhc7oSdU/zhFhzuIju0Jfb1274VWiAqByIyTJjjcy9LX+u/QJ/BAax40sef3BZYrp4pgRnSAvr8Y+ATFr6zucR/5aTSmTwnFWzKTLJZBa7ObJrZOw+f6LgxVOau6TFF+I7X9qHm3m2BrhrmKsixBvn+jCPnAt9mQsiANWArTTQ9To6CMM4hJr1PYkXkbETCkWfRrKnLrLUck9GJOXOyf5hihdq4XyR/UbyxyE8FSFluSeutDrBGn1phPG8TmhiasDHUG9YD6QNStFlKS+o4j1gMlJrs7pVpZJaAByPLDqv8ORYheLWqaOxSGu0dUN1GB7L4HrBNl5uOt6qx/k5E2TDyBHAX9lELCKtiQkrgT+7BnyTwpekOPwTQgVd34K0Y0XDvBPzr7CzGQjODoNcvzB15JFTORv68vA0AubaGsNsJbPfh4MwCWEGKFfVx3LYAdW7fcspjygdXMoia7E3hN4A/sLJX4Xq5rxMFs6Nqmk7suWw5/iCIT45gVrFzY/bzGS7p5PquoejRdzh5C5grlMYUzx7hmJgXO1bqLYKY2b6hSlNS/deFWI/ZZcIuOct2RJjLNFf1sQWODMC0RUGZTMUZGoCe4vLpkftjbk7s2bbs6V2uEsuVJ53+JzNeJZNkoEjX5cTeNcWNppgDD/flBU835Wx+3a2pGRPqhq3i6gpHTAmZssKfPoAvAT/UWWUx9jWy0p08RMx4Lq3zkh6Uib/ZexxlVa5syKpW/l6Qsv3C7mI4L7tz9fNdrk890eNxjs5m45iy9IXaV5RFqWR1SU3kYkjFnDnGpCxtQ7BSqoV5IefGfEs3Z7I3KJ51qlM+6i7Ag6ycAsdE78/qD961EyhrqhoNHnXXqJNOUofH1NaasACjq1SIEivgasVu5HCwPShMsweIYaFQiN3D5xeELe8fypd3vT0PhFZlxlCmKmaKikVDB/a8hW0CJ0hZ5pAYqLerTSYtqt/5cwCijxr1XtDgM6YJU7FkuvEBPW4myFk0Vm1MU7EjK3tf8DFOFeiRvnS+GVU0/vcC39Tf1w9Q58iIu50WtUAIybZMh/E9+MwOfPpyuZBwvVNNNi/gNiFih3VcSENXxKqhsNPmu+D76kDk8cd9gCch/rHjk8v/HeT8HXL8wXLmNpAc0BBodcrvRlCU9C5idrz7nds/hGsJnI5FzHsZvNDN6vtGnms6oOqjAfHVp/Q1h6P8srCmH7fOxw2rEq0+Jta5xrn+/taIdKMuUsfsqXO1l4BgnSK+vmdVZLoKhKeEpS8BHMCDQ2DKMzVLwhsMgQM6ArPgFK6YUTEbmyNFmFGK4BtoI7Zw0RAPgUNcBnOj1iyPjq0muZs/+Gf/QbFF94YwZF6PjmE+EKumBXaIGWoYZB1YqEO96+qtC6TKvVavhyOSCfhS/+uEbN+U6btsiy1Th25N17YqS9uz1BSSGbDdhCxu0osE10bK+l0OeZ37YOGVQ+jvddnM4GQLXvygXqDQXEZTWR++DQFsay2lU896u5rF/Pyfwt95Ebj2bS4Dv3Njjn9xcwMI6AB15UQXiLgSoV7EDteMHGoX0l9Y2Xo9XGOgHIP1juFzG2qwcJe+LYYVzobkKnLNgTRuGUGQCYc4CJqVcxOAhNA0pL/zdCxI6PYmAeyUQgzWxRIC6wPLLmWP6mI/jZiCA2ICnUPdasMZVDzUx0mY+hOSZdmBQBl6dOc6jA90rgEsdcmbZyGQOc+xw0/+xIRL3GMut3z4UDizZZEgU/09ifgNTvP3X0ZYYVET1+raSwysafDl0iIshgkb88Mq8bBDAmDzVSqj2BGDKKU52kamqYd8JMA7YEyXgFZL0iatlYry9Q7fdVhgksxzMzN8+XCJ7D5sUKO5oCzebcyhRpEqXjZqhovTLRwmR0GHe9kAshWwZZhNhVQfSuxSeAGWnoQfBZhYsex6mV7xBJ46kfJGnnOjDkmEhj/kEE4hgn/2fhehZPlwDz7cOeSHL63ztYOM1hQ+vHWXVeCrjYt8xVfU/OFrd505MdDkKZoCiFa8l+GFV3cuwf5dYCejuerxLyXxyyqGLQcjbh6piTJXURaNQdMkDQaDbRzjvgaeiSd8eTSuVnxxObQWcqQy4sDAKK6LftsWx+Aqe/AA1upLXOsjNbLOUXS5mvYOmAdeOg28Sxj7l27Kdw3gBz4k67nPKZ5chJTXCQ8p6aiVnO0QuBQF3AaSQtbR/1dh+QQ8XWT2Wra3EqoeOKWKpksYw4mnDglTeKBlBKR8gdEyKvWySoqznzvTygmsZJuh9fFMQ6rD90VnjGb3xPG2gxKgWLS3do2tfRioVeFkDGsa9TXORdlO0LgV12EcoKlK83Bb1YDSJGMMnJuH0wtwJhLzsc/F63WvJm8IxpVf1BEbiorzNlPiY4YwzQhWKWKWfojbXJk/yxe24NQqTPvwfwK0IQs7/L8zNcVKvTMQJAuBUBRGa4cFTVsB9vzkpxAWTsN4HzBFFMP3OAx2WL3YictHtEgdlph/S416pJ7OCCCDVi7+i4NjXu5kthhgibo2Enr6AF7fQ/YtAK59kxbwf/x5Mcj0yOGc5fqzli8Dg7ecABMzIuPlQ1jmWa40R9rMWtXqmviY/tJTVFrP7VPjGzSwl4KjS9sF8k1KNt13INIRiu/LQe4P/vbPWoDoHAXPNniWBqtwsXpzWmOXNona1HsWOcWBoaGHIog9uWws1C0NAvKaoRcv8Tlq9H9PpnjuNCx1BROslSjxPRPLtOOQhQ5cYhuBzBDI2NCK0bsWrrEseBLByTjjNJYzkcW0c12DXJtEYk52yn8taMJQCFmRZG7HROTUmIqmZEINhb7PS9cTOmNgBI9oc+/pp+Dz8HGXB+10u+M6yFqAoAzVPOb7H64dgE2kw1mkYkgAGMNkqGwmLQ1sUassevBI26KF1UcyAaZWUcCIDV8ktlBplCFXbu/22RGfrDCuTInmMggguaWiiYaHRxGwaApl8kymVhpblyKKvQy2v8wHEvjnf69ZiCP3c+BgvigMt5e3+SpoGZc64zSCm6J8/4wtcfflAHY7kDAFAx8hhwT+DQ12WWPLHhVX3nQmqh1JIjvlynYi+fJYCAmZqh8nKpixRSBQV1ksSUuKpBzHTWyZ0vUv+x9VogryAQRtLSC6EJcCqbXAIZdZ4e0n4b3nqpM3udZwHQEE7CDOKMsql1nVL94oJzJDDV4eh7ysG/r+3YQXgdM2Y7gEj7TBERh6LEG+xMNdCDpNwVHJOwX7LSzeLigMvFoGAPzCF+Hrh0B8SZMQbsm1LpTEwcPv7DtEk5zrhZ4mv0d8drDMx1s7NDmQ5Tvs7gBZgJ2Ier4LrLUjJjTpI1Y2oilnJb9OAp303XsW9j0bgq+juFHo3jMMIDRUdNSPAqNz6svJgD24PcqFTi0LlblWBRkAl03Ol35RRONhwbLnBXF0bosBPGfhlZFbt5gAesBvGZh3BLYxV3L54BGYsV4vQPerD7mxsqhGmh3lEFE5v12OZthFnC3/2NId3wBJQXLjbFzkAAcjOUJNYNCSUA2WAtKR5P+EC23QLplBU+y+0zIiXSG2zfsuCUmttE1a0Cv8jH8L16dSmvUCTc4guTz+WdhDkldQL3QN+AqQsc9Pc0tIpRorvmYbTFgivyoEcE1rht7Shc/xbhZ5ZaFJzwAAIABJREFURXBh7Dr0nOH3WOd1umAavGHh64xgcdFru3YGBt6RiXUijqQPLOwmQrJbKKOommbyIZBo/aUNAx05Fa/qpaYre/NIqyU7+2C80CB0/sHbpax44OahcMzygwoXeaexuAxn2qKPnQ2FCF40lrHLwVgB6PHHr8Cv7VPWIPIxLLG8/QtTsNCgTUOjaz97X77+oEHjNKYsssg68IVkSmLL7MRJ6yx73qQbGrNmmeN3pwIH98rW7OIsvKcNdwZw9RKYCJpNGF49ZsGqyEWv9NpF74dbe8KmrtTlDA2Qxe/fucyzJw1M9pl1VbotnWsbBlaAICl1RZyVyF/KZfrkLBkozJGBYMfUo0YLcc4CAacsfBsBACO4YaHGE8V1aQpfQPWuMC4A8z5kf77GAh8wt2R9Q/jtYQCcgKmhQUMI+20KJ9XO2/DN984z4KMwF/OzXIOb8LAPv6wyUR/JTow6MVnsU8MAkZuymQpf+ksE/0ljk1cZ85U8h1EdikSiUuQJkeJe+V2qMeDRESJejJOOzW9WP5en6okwQLBEmu9WLSlOhHMvn044c14yRKNGVQw75xJw9OHPc8iHnodf+3Y6ixalfpYCd+BkamAMB/tAWC+il18aT1VyOODa4jyXA8tXI/j6HFC/SB+IJtBQS5h/wNE2x35wnj8iVZd+oCNhIEWkwpOyW3Goh9NjLebn/st/UMC5QShkxVUDa8CIOt+8CQsbhtoH4L9pIh1K44cFIAc4jVW2YGTaTKhhawHQZN/WldUFupb7Ivn3nbk+oLewKsFh/ZxUq1JJ74saD32qk/ZLp44tqcOWnefmNwI4B+9bhIaRc/huptynJ5R6bgkt9Cn5Efdynnyo6pZ6r7+zMc8XztX5vc9BPZmKHVEtX6mHSfNRIOLPep03HiAsLEfsvBiIXB0XirN/6+KvAfB67YKQKhpAXEgmdbvD83o4X8HwN3ZDsKIvADCE1S0Ktjrneew3DuX9NzFiMeuIPXNiDFeBa0OwPZBipjkxlm7T0o2olL2MuzKBjj7/IlNO1eBZMk7nOa+wxN+3zzOw0JiHn+YBbRUm7zvduFk+7386+S1hv1aKSU+Bz9VeYD+WxpULffjI5BqhTuONLlxrnuLaCSHTewOU1ckkn1M1IbUS0OhG1tAWwFOLw8MmkoN1Arg6Odps1RHz4UwcVDHuAVs5pamrD0ONuQ/OAkYo9hxwLpRyB4XEU4eNiUBkwoh75iSTROXuqCoaGE4ySgfFX1Arm7R71YCNzqEwc0+AsCP6i019tYVaDj9yEdqnoR6Vjqi+C2BrrwGGewrEJhHri7XSbTpQbXgLXt8SdWNSzmJmBEIwhmgAFKJHbCCbl1KeVAsM4EzJ2KRTEyMiRoTkBODVVZdxAcs/WoODnsDbALSgdRLe038AFr6Qd3lAG6wKi4Eonm8DV6clRznXgnNtGBq4cxBzV4PzpIqkqNltgK50Jk3p0GOeAbDOHo+Q7RlxSmZiAkrL7RJOu7qkYtuhrv3JK/D76bu5cN7AbbhbIF4g+zMnP18YXoKxpKpYIgi71aiABmAlzOVeL6ETe2EZGqcSNiAhPlLeHuABGXaSVB55rCN29vPPUwJxEUn+aJ2Cv/UpxzhiRoei7LyawRPh4x6rw1n41BpmnQWmAdCWmifWQAZzRnU1MxPH35/MxKD5hQTcGEol62NShCHmMywzZIFR3sHvtfw7CoDnDfxA5xH0U17T75a6wI7hwlydL48ddVkr7l2hh/v0IwFwXj63B2J3tSRSIdes8+P/MfxVJnBdFLY6Rxuau/Eafn51LpnzDm56TwKed1Yue9t/yCxSqCl4Hjg7D//3vtwoOJ1yuh6wtCqren3cKcNNAwMsMEQtqGtzmAh+ZHlE21nVMmAUa650AjdguQPRBfn6bnqRu8CFs8Bgu1B8WlD0trRDuHoHIILlmV7YdaSuaFSHWgx7OQwOEIVWMg/Rwz62mkroueMLvDmtRLkPHG6TAjVFpQ+4aoFA9L4LJax/80swcYLWohTt+9gzE2CHM0CP89wuKPhJ3tQTv256NIGbupWZ2wOrTiE3Kc9davUpoTIIEBY2D+z4VLnTKDY0ata5r3KyqamCBUiYuvcCl3u8AARt5/3jSO38lid+dmIh3bdKvrMKfHwJPk6Nv/uwWgbg9AVP1nBndBc+uZ5zFrhkM2kd/IxsiR2MYPmKiL6qUk3vCxqbSP0ERp54270koCgJ5QLhhsE6fxqv81/4/N9ArBV4TA6dgj3lOAe6/8gfmYM9DLw/4nSjwxmPAN3od8G0qK2rUe7QkvWUcq5CpwUvzSivh9FpzXkY45rApmib7+YZSGAvzXhfuwftjMstMHPQDxZ5mMPhEDgJZr4tXvR0RGCnFQmAEPE3zgWQLwrLOkwZ+l0HTei5zaGg921TZFXSBbpdyNUAkstxb+vX0UXVrpOZgK+feBGePlX9rIy+mIMs4JaaU+dMT+NdnI7gkajidHgjBeIurIisGpsi96bwE+zuwmtW9JVgGcwDjo5mCGshJxqwHEF/ANkglJPWdjm78JfWKM2gi1QoqLNyzRHSBc6cD/kkOezB4f0yLOKvrWn+sXa5n1zzngFAzpln4N9VOLXH8q5HjOTYNIAowBAU9s/a88+LUf51lesCI63EFHApKYRj5tpjbAR2Rw7LPzXAXFPzN2uwoUaM1xzTWIRbd4CAmqPk+6Vbrl0LaH9ynnMx0rp9NIREnVOBhNzUgrrqG5kwVa0VIZacBJgUHva5GG5PQEiIiARiyFkt2N1oM4VgnrWTo5KzJR0ghCXJ7jGgvcsavEtDlC/U4bPAtkK5A6oRQ+NUBNrUEvDyO9AdUYxsgDscWruOKFyDOpxztNf5I9e9B/zy83DzlsBzweNcQ5YZYhg4VqTc56RByqffUZxvlNwqKJCujTFOCjLAMqxcEp4Z9cFktE1Co0iWPYDMshhKoBq5/n8WwPKD56gmIOkwCNVhDm6HVRL2YlxqTbOBnn8MQEaXGafOoqW7WqoCp/WdRS6bA1wEywtCGDemiEjQRMOXqbQhcwQrRuP67ut5e6JF+ztTlYzmNebfkNdDuACPMtnpiQrB/7kqrb9UIaHAiwWBpvaEJEc438PgRnnZxadyFnFxTz1WYzgZwrMJ/Pvz8NP7kG4mUgKmQ8W8FSq8AAxTloAaMecbhntBSI9QjF0TAVhfHV5hHdZOlzKtBTbLkM2CRtU7IeeA96hO2VyQeLd/od+7Jzy2Qp/FJfwDcGFOJn1oJMLiQGF4Pj4ej6LIM6w9uwijRc+C1hcRfxxoyEVUeqNBm+gdwnAErSbaJQ9gocwByCCodRBy1gVcNlgkciEWzC5lZskKZNsQCM5t5SVxN8ZwJK/4mLESLVavqjiHqzHc7wKGxyrOMool6YgoG3FCje6SOyRjoQoxanlTIuGczLmWidRLRs4zrOPECpzowZtuJytGddeOaAD1MiPsLyNBo4W1dcaZ6EYIdC9QhHydxgeJYR1hfFI6apffXwz5r1krBQKdyvPzKvv3jw/+cKMGPKetBLZPyvsaM9LqDRX5AiNMe9Gb/jJQ81UJE/Fpm/MQTyfoiHoFcCMV/+A+QADfvyQXGqSmNcC9UO0bpqjNUHqBPFCbN3/jV3UlE1oRvN6HvRFMmYPanDqDLOemd4oQBMtCwZ32jBTpdRUhUiue140uBK05oKG6QiirUMdK30ZY11nUQKtVWikZWBhNJQDOlDO/diB9BIrNGPZwBUCV98ilTwoduXVTLntivtw+1wH01EnZhcKeZUPRTzL4zvUMsLQdsgfV3uQLfsRwzYl7Exb7PSI9wFkEWeM8oxTevC2H8ClP6X8D2FJ2ugC8RwO7xoS8jYGFbtkpYyqlCT+gB8IfU2pYGzBAc0NQ7uOsdjPy/sll7eJKzokWzNuE2ljTQzFYaowMXKMl3D5QLjeCPacrDeTas8pUk4O88i6HaN9JIyLTogmcaltaSlr2EDvB2VB8E9ZWb5wi76MlzDYoUukitrRaCAiJ2MrKGLSFhpDcQEXw9RBqZ4BaDRM1ygNg3d2IGlBJYX9aS3zfRBxBD2GRGo8Ihf2nY6a87tlmcvrIiR57cJdhiMwi7VNteXkGnU6T7nlYa4Zl0x39SXPYmSKZdQ7jGhAsKHR7IbnTmWpwYQ12miXgCRvAqniKDJJn6o1zZzUe0imgSU4IrF0uw4a988aZNcDmnD0VSqG619KC7o3agmyuSn1mWqUxIwRDi/HiImaqrHgJjVmHKxdiOT+HOpHZtNIa9ONWwTGaCD1wI6bGC9pfCRe8WexuWYT6sn7yRx4TbOjhNU1mOIdh3gC2RhZCFhiwNXIPOQOHeVYc560e0nCuVRYMU7AC8PQawsEC0W+fA67ulJHmCcIdl5EqkacAGpAshbCmD8zA5jVuTwMPudpAHwaWxsSppJ0C9gESgzTPLnPAcgBmo16IrcebzlV+9RcCmKuP7hThLPVMLB02hMyElWe1X/kybAp2jtW11wfum7qe33VWN9zDW9CUCT9ziQLA/oEI3KTcLUWMPhhTyhOFtUJlYCeGSOq2PvARrD/lmXXdSBOnf8mowYnVHL9iVWDtEXBJD4JcStYoEvV1RlYjt9OgBZEpcSwTBIpyD+uyQKpQNI0g5GEiPP9Qz8QV6aOQTSAcqP6ZWvFrKUP+JrBBwhMMqQhv9fIXY6v9LNIENh0s1sqCBS7372RDkraK4On5eZgTJM77wCjDMiFzgvpIfvYMZHVTkdEBOIBz3+dQJfds+RHTsKUpMfLtPsAr8uBTV+Q5iamaWh2Z2tN3G3IaeJn6qm6+sQtbus4ohBNmwJUTeEl0NdzuVtJiZ4pU+Ptv7m7eKffUyibk5uiBMEA4ugpfPiyQoA/sGkP7Q+9npeVcanmBRJJWrOTKi8wtD4RnTPfqABkzU9nMQTiB6aG2zx3CQJ841wYzT+Gbd2iz0PJK4LgJ2RzyrGjdXACj+CXEBNpQ2INUH8jSKTaxOs8a06apcN/AQpRZyrq6gYa7G0wDfP1nVx4hx97I+sUgY8V6k1G2lM0TeH1SclHnBEjTKjznAGNdDhJWajYw2XXNq2XUgbrjGmtNrdWqBKuhc0+SajjGJjCEw7GWtl5UPj8Pq3MBAcFRV7AxWI+TZSAJ16GBXXnV1BZfV4e70VLKucUHerkBCHk1g+RteN8TGUS5p4/aYtWB/453OhAPvQMx8Q5EbsIyEpKqH8g+SOHObaZA570XKjJY4EXdS9BoVi7iCIcwQpagBL71DpG72L1cH5Xojicat10LKVutVlYpvQeqOmYOZGUZk8r1BkyEMUZ7PMsoxKQ0kRKzuj/HHghry9AXlwOBkS46vqofQuAAaipMSzhEBqaukCoOGJUIVHvHg9OMvzKPBadGQDKRohZOKWmfDYgigy9UG9QO4ohPnpV5LG7oHFNNZKpHDnoBgY/AbuiBcHpayffLa6sCizcqz5s5ENbDec3UlP13GVFHH2dSD1YzVkjf2hQVq5yCIcTEzkCUkbhYeX2Jc6YFpyLMqQtF/+LH22hmhr8uo0H1x9VYOm54COPqAbgASzcqiP+4SLjZUZAaQ1ntKihw0EKBJIlyIBdbZEeWqZeXeEzUQLFkp4f7gXXfdYqOzzu5Tw9D7jjFCtVwc/fAlOIANVAH5kZAQdJcC97Mn6H/2TuPRnzMnhu935aPqg6LwRRR9t99vIM10W1XDpARmBkcOobrpD4tmjmFNi+NolE+KGWHoOqMpa7QSahyi9mXVYHjIPO47/XFjMBCOoNG1oD18h/9uQdHfpEnu7cd3cuaN52AtJL3DXVXzRkoNY0Aa4WV50aSitw9h1oQgh4sOz/E0EJgCu/yBn5ZynJkicT7ofezyGP33Oa+SFAuJYdqQSqfK2hKaUG1dFRgH1BajaCSZupGOIPJ9vHTfPzwb6jgqjw81lzZjKB6bVC57LsP49aXzXCm4y5+XJiR3OhgUl7l5JgDpBxIiJK8RFE2xtaio2T5MTO1OFYU+x/LXPOMNK9Vn1MR8kqhKndzduz1yMkESAVRMsq6S8HsZTkREdM+WAKGQ+jT4WDdckYKq8vL9txbxzBJJJSgWLKad2zIjlYQ6JCDtWQW0kbINeByjHqIc4gNqQrKdqileyywAyaWiFRqFFl7cWhElJqx29vDUhcrpuIZEezM/0eGB4vKNWEg5yjNsKQEKruFthR1J+LPPuZRJSkKZsWVXMy/5sheqQBlIXKH13GWTCm5C7PAaihBWor/pkoEj86ldjwwGuVnWVo9p/4Mo6inti3TZLANQRB6FwewpjcPEtJBAjS1KVz5kBmiVBlurVUuFWLRGvFun1UcsO/AsrMEwuiYN420NlMsi51FJrM7lvkFY2pRV7auhZisHmWMjSqZTArlP0AqwbW9ihLL04zrGN7YS7GBfPGCAjoawXSquRAperBMYfGwADVYMBTFeoceeXRWVuPk2wQc+bYTK11BPSDajKrH8HEd2Y8b6itxyreMEOs2Iyni7txX1eEj8pFhK19keeAFO3934TpKIGGiJc3d4yYl8zAhBHExb/d/GZgxK5BlQIcsNZV1ZNYe213WC8weEbh2pt44vC7/n9UCFBMGjLZDoMwWExZvC2qH5gNXTnHq/VFMTKHqKVfHsmdL1TNmymszCaspZ23Fhj/OYFf9OZeb20QW5oeA3WaTi9TmA2jB6qhsY2nQc+50o9k214dS5v0NN6cAdvSahQbURnKASrhb8n1TdkZ0CrGT4T2EquHg4lGEgYbZ1hpF0pU/9tWx2QVpbUt5sI7TZywwyWW1U1Np4AnAiqNyOWylvcqhmI4oEmmONLCfHeqwSYH+JMeEQvBaQBBGFSZXuccbE9+yNPKNhRY6R0NwjouBlqKzYOlDc666WBWLDVaKP+uIIrfq7T7dCMa2e+wEHVWQ3POcETtIYeZ6VTwrEoK1a6YijHHPdCK7UwZDCWlyVR2iyJDqviQ5xQlxRN94pj2pKZriqlQ4tusSRxbm5f4gl/pkbh+bPGDcb0BnmUY4pe0iH5Wc5wSyplkkDOQdn1iBmwVbLoHZdn0eLJzXeV/Hig1HF7CFl5B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          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SKR-01-S6lu7",
        "colab_type": "text"
      },
      "source": [
        "As you can see, the composite image retains the scenery and objects of the content image, while introducing the color of the style image. Because the image is relatively small, the details are a bit fuzzy.\n",
        "\n",
        "To obtain a clearer composite image, we train the model using a larger image size: 600 by 900 pixels. We increase the height and width of the image used before by a factor of four and initialize a larger composite image."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "attributes": {
          "classes": [],
          "id": "",
          "n": "19"
        },
        "id": "Pl81PuJJ6lu8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "image_shape = (600, 900)\n",
        "_, content_Y = get_contents(image_shape, device)\n",
        "_, style_Y = get_styles(image_shape, device)\n",
        "X = (preprocess(postprocess(output), image_shape)).to(device)\n",
        "output = train(X, content_Y, style_Y, device, 0.01, 300, 100)\n",
        "d2l.plt.imsave('../img/neural-style.png', np.asarray(postprocess(output)))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BE-pcC8PZViG",
        "colab_type": "code",
        "outputId": "995cedc4-a5cd-4da7-ff1a-99c397aa1923",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 258
        }
      },
      "source": [
        "d2l.plt.imshow(np.asarray(postprocess(output)))"
      ],
      "execution_count": 226,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fe3519beeb8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 226
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 252x180 with 1 Axes>"
            ],
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      "source": [
        "As you can see, each epoch takes more time due to the larger image size. As shown in :numref:`fig_style_transfer_large`, the composite image produced retains more detail due to its larger size. The composite image not only has large blocks of color like the style image, but these blocks even have the subtle texture of brush strokes.\n",
        "\n",
        "![$900 \\times 600$ composite image. ](../img/neural-style.png)\n",
        "\n",
        ":label:`fig_style_transfer_large`\n",
        "\n",
        "\n",
        "## Summary\n",
        "\n",
        "* The loss functions used in style transfer generally have three parts: 1. Content loss is used to make the composite image approximate the content image as regards content features. 2. Style loss is used to make the composite image approximate the style image in terms of style features. 3. Total variation loss helps reduce the noise in the composite image.\n",
        "* We can use a pre-trained CNN to extract image features and minimize the loss function to continuously update the composite image.\n",
        "* We use a Gram matrix to represent the style output by the style layers.\n",
        "\n",
        "\n",
        "## Exercises\n",
        "\n",
        "* How does the output change when you select different content and style layers?\n",
        "* Adjust the weight hyper-parameters in the loss function. Does the output retain more content or have less noise?\n",
        "* Use different content and style images. Can you create more interesting composite images?"
      ]
    }
  ]
}